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The path to China's zero-carbon target: Implications for the relationship between de-coal, economic output, and carbon dioxide emissions
No full textThis paper investigates the asymmetric impact of de-coal in China on economic output and carbon dioxide (CO2) emissions through a nonlinear time series simulation over the period 1965-2021. The research also estimates the effects of using renewable energy as an alternative fuel to replace coal. The results show that the reducing effect of de-coal on CO2 emissions is stronger than the increasing effect of coal consumption. The negative impact of de-coal on economic growth is weak or insignificant. While renewable energy contributes to economic output, it has a reducing effect on CO2 emissions. At the end of the paper, China's targets to achieve carbon neutrality and de-coal are discussed based on the findings
Environmental sustainability, medical waste management, energy and medicine consumption of the surgical intensive care nurses: A qualitative study
No full textBackgroundIn intensive care units, it is noticeable that there is intensive use of resources in the treatment and care process, leading to a significant amount of waste generation. In addition, the demand for intensive care, increasing life expectancy and surgical interventions, complex comorbidities and ecological crisis make it necessary to make critical care more sustainable.AimTo explore the perspectives of nurses working in surgical intensive care units regarding responsible medical waste management, energy and medication consumption.Study DesignThis qualitative descriptive study was conducted in surgical intensive care units of a university hospital in Turkey in November 2023. Twenty-three nurses filled in an introductory form and participated in a semi-structured interview. Data were analysed using inductive content analysis.ResultsThree main themes were determined: environmentally sustainable intensive care, prevention of waste in intensive care; responsible consumption and recycling; suggestions for institutional and individual behavioural change regarding environmental sustainability.ConclusionsThe majority of nurses lack knowledge about sustainable development goals. However, in the intensive care unit, they provided effective and creative solutions for medical waste management, energy and medication consumption and individual and institutional behavioural change regarding environmental sustainability.Relevance to Clinical PracticeSustainability strategies should be created in institutions to ensure responsible medical waste management, energy and medicine consumption and reduce carbon footprint. In accordance with this purpose, 'Green teams' including unit-based doctors, nurses and paramedics should be established. Training should be provided and awareness should be raised to reduce energy use resulting from heating, lighting, ventilation and air conditioning
Skin tears in older patients in intensive care units: A multicentre point prevalence study
No full textBackgroundWith the ageing of the global population, it is predicted that the population of older adult patients in hospitals and intensive care units (ICUs) will increase. Because of health conditions, care practices and ageing-related skin changes, older adult ICU patients are prone to skin integrity problems, including skin tears (STs).AimTo determine the prevalence of STs and associated factors in older patients hospitalized in ICUs.Study DesignThe study is a regional, multicentre, point prevalence study conducted in five centres in the five largest cities in terms of population in the Central Anatolia Region of T & uuml;rkiye. Data were collected simultaneously in each centre on the same day. The list of patients hospitalized in the ICUs on the day of data collection was drawn up, and 200 patients who were 65 years of age or older, were hospitalized in ICUs and agreed to participate in the research were included. The researchers formed an "ST chart" to record patient demographic characteristics, clinical variables and skin assessment.ResultsSTs were detected in 14.5% of patients in ICUs, with 72.5% of them having stage 1 ST. A significant relationship was found between individuals' average body mass index (BMI) (p = .043), age (p = .014), length of stay in the ICU (p = .004) and having ST. There was also a statistically significant relationship between skin temperature (p = .002), skin turgor (p = .001) and ST. More STs were observed in patients with cold skin and low turgor. The prevalence of ST was higher in individuals with a history of ST. Additionally, there was a statistically significant relationship between consciousness (p = .014), incontinence (p = .006), the Braden score (p = .004), the Itaki fall risk score (p = .006) and ST.ConclusionsIn this multicentre point prevalence study, the prevalence of ST in older patients in ICUs was 14.5%, and many factors associated with ST have been identified.Relevance to Clinical PracticeGiven the insufficient information and attention to STs in older adults, the study emphasizes the importance of addressing STs. The impact of STs includes increased treatment costs, length of stay and risk of complications. Therefore, understanding the global extent of STs in ICUs and developing effective interventions for prevention and management are crucial
Best Individual Guided Immune Plasma Algorithm on Solving Path Planning Problem of Unmanned Combat Aerial Vehicles
No full textUnmanned aerial vehicles (UAVs) and their variants equipped with sophisticated weapon systems called unmanned combat aerial vehicles (UCAVs) have completely changed the classical war strategies and concept of military operations. For guaranteeing the autonomous flight safety and success of the task being performed by these modern aerial vehicles, a path must be determined optimally after considering some kinematic constrains, existence of enemy threats, fuel or battery limitations. Immune plasma algorithm (IP algorithm or IPA) inspired by the implementation steps of a medical method gained popularity with the COVID-19 and known as convalescent or plasma treatment is one of the most recent intelligent optimization or meta-heuristic techniques. In this study, plasma treatment procedure of the IPA was changed with a newly introduced approach called the best individual guidance for short BIG that is based on using the most qualified solution found by the algorithm and three different donors when collecting plasma and BIGIPA was developed as a novel UCAV path planner. For investigating the path planning capabilities of the BIGIPA, a set of detailed experiments was carried out by using different battlefield configurations and assigning various constants to the control parameters such as population size and number of receivers and then obtained results were compared with the results of other path planners based on well-known meta-heuristic algorithms. Experimental studies showed that introduced treatment procedure gives a significant contribution to the convergence performance and qualities of the final solutions especially for the test cases with relatively high dimensionalities and BIGIPA calculates more promising, flight efficient, and safe UCAV paths compared to the tested algorithms
Gender differences in cyber dating violence among adolescents and young adults: A systematic review and meta-analysis
No full textDespite the growing body of research on cyber dating violence, a comprehensive understanding of gender differences in cyber-violent behaviors across developmental stages remains limited. The main purpose of this meta-analytic review was to estimate the direction and magnitude of gender differences in cyber dating violence perpetration and victimization by synthesizing results from various studies. The second purpose of this study was to examine the effect of potential moderators (i.e., continent, age, grade level, time frame, method of survey administration, the metric of the outcome, study design, publication status, and publication year) on these differences. Various databases were used to identify relevant studies, including PubMed, Web of Science (WoS), Scopus, PsycINFO, ERIC, and ProQuest. Eighty-one individual studies with a total sample of 70,233 participants, ranging in age from 10 to 30 years (M = 18.94), were included based on the inclusion and exclusion criteria in the present study. Most studies were conducted in North America and Europe with the largest proportions from the United States and Spain. Results indicated that there were no statistically significant gender differences (women vs. men; girls vs. boys) in perpetration and victimization of cyber dating violence. Moderator analyses showed that grade level and sample age were statistically significant moderators of gender differences in cyber dating violence victimization. However, other moderators (continent, time frame, method of survey administration, the metric of the outcome, study design, publication status, and publication year) were not statistically significant. This study contributes to understanding gender differences in cyber-violent behaviors during adolescence and emerging adulthood and highlights the importance of some moderators when developing targeted prevention and intervention strategies
Breast Cancer Diagnosis Using K-Nearest Neighbor Algorithm
No full textBreast cancer occurs when cells in the breast grow uncontrollably and abnormally and develop into tumorous tissue. Breast cancer, which is highly prevalent worldwide, can occur in both women and men. It is much more likely to occur in women than in men. Mortality rates from breast cancer provide general information about the health systems of countries. Early diagnosis and treatment are of great importance in breast cancer. Breast cancers are divided into 2 types: benign and malignant. The correct classification of tumors as benign or malignant can prevent patients from undergoing unnecessary treatments. As machine learning methods have developed, their use in health systems has also increased. K-Nearest Neighbor (K-NN) algorithm is a machine learning algorithm used for classification problems. In this study, Wisconsin Breast Cancer dataset is classified using K-NN algorithm. The dataset has a total of 699 samples belonging to 2 classes, benign and malignant. In the study, the effect of 3 different distance metrics and 3 different number of neighborhoods on K-NN performance is evaluated over different metrics. The results are presented in tables and graphs.</p
